Data Research, Vol. 1, Issue 1, Dec  2017, Pages 1-9; DOI: 10.31058/j.data.2017.11001 10.31058/j.data.2017.11001

Clustering Cells using K-Means vs. Genetic Algorithm using Shape Descriptors

Data Research, Vol. 1, Issue 1, Dec  2017, Pages 1-9.

DOI: 10.31058/j.data.2017.11001

Faten Abushmmala 1 , Mohammed Alhanjouri 1*

1 Computer Department, Islamic University- Gaza, Gaza City, Gaza Strip, Palestine

Received: 28 November 2017; Accepted: 27 December 2017; Published: 5 January 2018

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Abstract

This paper interested in clustering red blood cells, these cells are in form of digital images of blood films, a comparison made between Genetic Algorithm (GA) and K-Means behavior/performance in clustering. The data set consists of shape descriptors of the cells shapes, the original number of samples are 100 samples. Each sample provided us with at least 10 cells (shape) with total number of 409 shapes (cells). The Genetic Algorithm shows better performance than K-Means in clustering these cells into two clusters (Normal and Abnormal) with success rate 99.48% where K-Means gave 83.16%. While K-Means shows a better performance in clustering the cells into four clusters (Burr, sickle, teardrop and normal cells) than GA where K-Means gave 86.74% and Genetic algorithm (GA) gave 83.2 %.

Keywords

Cells Shape Descriptors, Cells Shapes, Clustering, K-Means, Genetic Algorithm

Copyright

© 2017 by the authors. Licensee International Technology and Science Press Limited. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

References

[1] H. Parmar; B. Limbasiya. A Review on Genetic Algorithm-based Text Clustering Technique. International Journal of Advance Research in Computer Science and Management Studies, 2015, 3(2), 81-85.
[2] Peng-Yeng Yin. Pattern Recognition Techniques, Technology and Application. Veinna, Austria, 2008, 626.
[3] Liu Z.; Zhao C.; Wu X.; Chen W. An Effective 3D Shape Descriptor for Object Recognition with RGB-D Sensors. Sensors (Basel), 2017, 17(3), 451.
[4] M. Eitz; R. Richter; T. Boubekeur; K. Hildebrand; M. Alexa. Sketch-based shape retrieval. ACM Trans. Graph. 2012, 31(4).
[5] M.E. Yumer; S. Chaudhuri; J.K. Hodgins; L.B. Kara. Semantic shape editing using deformation handles. ACM Trans. Graph. 2015, 34(4), 86.
[6] M. As’ari; U.U. Sheikh; E. Supriyanto. 3D shape descriptor for object recognition based on kinect-like depth image. Image and Vision Computing, 2014, 32(4), 260-269.
[7] P. D’Silva1; P. Bhuvaneswari. Various Shape Descriptors in Image Processing – A Review. International Journal of Science and Research (IJSR), 2015, 4(3), 2338-2342.
[8] J. MacQueen. Some methods for classification and analysis of multivariate observations. Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, 1967, 1(14), 281-297.
[9] W. Barbakh; Ying Wu; Colin Fyfe. Non-standard parameter adaptation for exploratory data analysis, University of the west of Scotland, Scotland, 2009. ISBN: 978-3-642-04004-7.
[10] J.H. Holland. Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor, 1975, 15.
[11] P. Kohn. Combing Genetic Algorithm and Neural Networks. M.Sc. thesis, Tennessee Unive, 1994.
[12] Sh.A. Rasheed. Genetic Algorithms Application in Pattern Recognition. M.Sc. thesis, National Computer Center Higher Education Institute, 2000.
[13] R.L. Wainwright. Introduction to Genetic Algorithms Theory and Applications. Addison-Wesley, 1993.
[14] M. Schamidt; T. Stidsen. Hybrid Systems: Genetic Algorithms, Neural Network and Fuzzy logic. Univ. Aarhus. Denmark, 1997.
[15] N.K. Pareek; V. Patidar. Medical image protection using genetic algorithm operations. Soft Computing, 2016, 20(2), 763-772.
[16] F. Liu; G. Zeng. Study of genetic algorithm with reinforcement learning to solve the TSP. Expert Systems with Applications, 2009, 36(3), 6995-7001.
[17] S.M. Elsayed; R.A. Sarker; D.L. Essam. A new genetic algorithm for solving optimization problems. Engineering Applications of Artificial Intelligence, 2014, 27, 57-69.
[18] Y. Deng; Y. Liu; D. Zhou. An Improved Genetic Algorithm with Initial Population Strategy for Symmetric TSP. Mathematical Problems in Engineering, 2015, 3, 1-6.
[19] F. Abushmmala; F. Abushmmala. Processing Overlapped Cells Using K-Means and Watershed. International Journal of Intelligent Information Systems, 2014, 3(1), 8-12.
[20] F. Abushmmala; M. Alhanjouri. Colour Based Segmentation of Red Blood Cells using K-means and Image Morphological Operations. Journal of Advanced and Innovative Research, 2013, 2(11), 344-350.
[21] F. Abushmmala; W. Barbakh. Color Based Segementation using different versions of K-means in two Spaces. Global Advanced Research Journal of Engineering, Technology and Innovation, 2013, 1(9), 030-041.
[22] G. Karkavitsas; M. Rangoussi. Object Localization in medical images using genetic algorithm. World academy of Science, Engineering and Technology, 2007, 1(2), 72-75.
[23] P.J.H. Bronkorsta; M.J.T. Reinders; E.A. Hendriks; J. Grimbergen; R.M. Heethaar; G.J. Brakenhoff. On-line detection of red blood cell shape using deformable Templates. Pattern Recognition Letters, 2000, 21(5), 413-424.

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